An official website of the United States government
Our understanding of ownership influences how we interact with objects and with each other. Here, we studied people’s intuitions about ownership transfer using a set of simple, parametrically varied events. We found that people ( N = 120 U.S. adults) had similar intuitions about ownership for some events but sharply opposing intuitions for others (Experiment 1). People ( N = 120 U.S. adults) were unaware of these conflicts and overestimated ownership consensus (Experiment 2). Moreover, differences in people’s ownership intuitions predicted their intuitions about the acceptability of using, altering, controlling, and destroying the owned object ( N = 130 U.S. adults; Experiment 3), even when ownership was not explicitly mentioned ( N = 130 U.S. adults; Experiment 4). Subject-level analyses suggest that these disagreements reflect at least two underlying intuitive theories, one in which intentions are central to ownership and another in which physical possession is prioritized.
more »
« less
No signatures of first-person biases in Theory of Mind judgments about thinking
We readily get intuitions about a problem's complexity, how much thinking it will require to solve, and how long it should take, both for ourselves, and for others. These intuitions allow us to make inferences about other people's mental processing---like whether they are thinking hard, remembering, or merely mind-wandering. But where do these intuitions come from? Prior work suggests that people try solving problems themselves so as to draw inferences about another person's thinking. If we use our own thinking to build up expectations about other people, does this introduce biases into our judgments? We present a behavioral experiment testing for effects of first-person thinking speed on judgments about another person's thinking in the puzzle game Rush Hour. Although people overwhelmingly reported solving the puzzles themselves, we found no evidence for participants' thinking speeds influencing their judgments about the other person's thinking, suggesting that people can correct for first-person biases.
more »
« less
- Award ID(s):
- 2045778
- PAR ID:
- 10573978
- Publisher / Repository:
- Proceedings of the Annual Meeting of the Cognitive Science Society
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
How do people use human-made objects (artifacts) to learn about the people and actions that created them? We test the richness of people’s reasoning in this domain, focusing on the task of judging whether social transmission has occurred (i.e. whether one person copied another). We develop a formal model of this reasoning process as a form of rational inverse planning, which predicts that rather than solely focusing on artifacts’ similarity to judge whether copying occurred, people should also take into account availability constraints (the materials available), and functional constraints (which materials work). Using an artifact-building task where two characters build tools to solve a puzzle box, we find that this inverse planning model predicts trial-by-trial judgments, whereas simpler models that do not consider availability or functional constraints do not. This suggests people use a process like inverse planning to make flexible inferences from artifacts’ features about the source of design ideas.more » « less
-
People take to social media to share their thoughts, joys, and sorrows. A recent popular trend has been to support and mourn people and pets that have died as well as other objects that have suffered catastrophic damage. As several popular robots have been discontinued, including the Opportunity Rover, Jibo, and Kuri, we are interested in how language used to mourn these robots compares to that to mourn people, animals, and other objects. We performed a study in which we asked participants to categorize deidentified Twitter reactions as referencing the death of a person, an animal, a robot, or another object. Most reactions were labeled as being about humans, which suggests that people use similar language to describe feelings for animate and inanimate entities. We used a natural language toolkit to analyze language from a larger set of tweets. A majority of tweets about Opportunity included second-person ("you") and gendered third-person pronouns (she/he versus it), but terms like "R.I.P" were reserved almost exclusively for humans and animals. Our findings suggest that people verbally mourn robots similarly to living things, but reserve some language for people.more » « less
-
null (Ed.)Abstract People spontaneously infer other people’s psychology from faces, encompassing inferences of their affective states, cognitive states, and stable traits such as personality. These judgments are known to be often invalid, but nonetheless bias many social decisions. Their importance and ubiquity have made them popular targets for automated prediction using deep convolutional neural networks (DCNNs). Here, we investigated the applicability of this approach: how well does it generalize, and what biases does it introduce? We compared three distinct sets of features (from a face identification DCNN, an object recognition DCNN, and using facial geometry), and tested their prediction across multiple out-of-sample datasets. Across judgments and datasets, features from both pre-trained DCNNs provided better predictions than did facial geometry. However, predictions using object recognition DCNN features were not robust to superficial cues (e.g., color and hair style). Importantly, predictions using face identification DCNN features were not specific: models trained to predict one social judgment (e.g., trustworthiness) also significantly predicted other social judgments (e.g., femininity and criminal), and at an even higher accuracy in some cases than predicting the judgment of interest (e.g., trustworthiness). Models trained to predict affective states (e.g., happy) also significantly predicted judgments of stable traits (e.g., sociable), and vice versa. Our analysis pipeline not only provides a flexible and efficient framework for predicting affective and social judgments from faces but also highlights the dangers of such automated predictions: correlated but unintended judgments can drive the predictions of the intended judgments.more » « less
-
We examined whether, even at zero acquaintance, observers accurately infer others’ social network positions—specifically, the number and patterning of social ties (e.g., brokerage—the extent to which a person bridges disconnected people) and the trait impressions that support this accuracy. We paired social network data ( n = 272 professional school students), with naive observers’ ( n = 301 undergraduates) judgments of facial images of each person within the network. Results revealed that observers’ judgments of targets’ number of friends were predicted by the actual number of people who considered the target a friend (in-degree centrality) and that perceived brokerage was significantly predicted by targets’ actual brokerage. Lens models revealed that targets’ perceived attractiveness, dominance, warmth, competence, and trustworthiness supported this accuracy, with attractiveness and warmth most associated with perceptions of popularity and brokerage. Overall, we demonstrate accuracy in naive observers’ judgments of social network position and the trait impressions supporting these inferences.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.31234/osf.io/q79bz
